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MGMT 276: Statistical Inference in Management Spring , 2013PowerPoint Presentation

MGMT 276: Statistical Inference in Management Spring , 2013

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MGMT 276: Statistical Inference in Management Spring , 2013

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MGMT 276: Statistical Inference in Management Spring , 2013

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Welcome

Instructor:Suzanne Delaney, Ph.D.

Office:405 “N” McClelland Hall

Phone:621-2045

Email:delaney@u.arizona.edu

Office hours:2:00 – 3:30Mondays and Fridays and by appointment

Homework due – Tuesday (April 2nd)

On class website:

Please print and complete homework

worksheet #14, 15 and 16

Hypothesis testing with t-tests

A full week is allowed for this homework because it includes the design and completion of an original piece of research – please plan accordingly

(Please note this worksheet accounts for three homework assignments).

Please click in

My last name starts with a

letter somewhere between

A. A – D

B. E – L

C. M – R

D. S – Z

Please double check – All cell phones other electronic devices are turned off and stowed away

Please read:

Chapters 10 – 12 in Lind book and

Chapters 2 – 4 in Plous book:

(Before the next exam – April 9th)

Lind

Chapter 10: One sample Tests of Hypothesis

Chapter 11: Two sample Tests of Hypothesis

Chapter 12: Analysis of Variance

Plous

Chapter 2: Cognitive Dissonance

Chapter 3: Memory and Hindsight Bias

Chapter 4: Context Dependence

Use this as your

study guide

- Logic of hypothesis testing
- Steps for hypothesis testing
- Hypothesis testing with t-scores (one-sample)
- Hypothesis testing with t-scores (two independent samples)
- Interpreting excel output of hypothesis tests
- Constructing brief, complete summary statements
- Hypothesis testing with analysis of variance (ANOVA)
- Interpreting excel output of hypothesis tests
- Constructing brief, complete summary statements

If this is less than .05 (or whatever alpha is) it is significant, and we the reject null

df = (n1 – 1) + (n2 – 1) = (165 - 1) + (120 -1) = 283

- A survey was conducted to see whether men or women
- superintendents make more money
- The independent variable is ________________
- The dependent variable is _________________
- 3. Who made more money men or women?
- 4. Identify the two means and the observed t score
- 5. Identify the p value and state whether it is less than .05

A survey was conducted to see whether men or women

superintendents make more money

Are both p values less than 0.05?

1.37834 E-05

Equals

.00001378

4 zeros

6.8917 E-06

Equals

.0000068917

5 zeros

A survey was conducted to see whether men or women

superintendents make more money

A note on scientific notation:

“E-05” means move the decimal to the left 5 places

E-06” means move the decimal to the left 6 places

1.37834 E-05

Equals

.00001378

4 zeros

6.8917 E-06

Equals

.0000068917

5 zeros

A survey was conducted to see whether men or women

superintendents make more money. The independent variable is

a. nominal level of measurement

b. ordinal level of measurement

c. interval level of measurement

d. ratio level of measurement

A survey was conducted to see whether men or women

superintendents make more money. The dependent variable is

a. nominal level of measurement

b. ordinal level of measurement

c. interval level of measurement

d. ratio level of measurement

A survey was conducted to see whether men or women

superintendents make more money. The independent variable is

a. continuous and qualitative

b. continuous and quantitative

c. discrete and qualitative

d. discrete and quantitative

A survey was conducted to see whether men or women

superintendents make more money. The dependent variable is

a. continuous and qualitative

b. continuous and quantitative

c. discrete and qualitative

d. discrete and quantitative

A survey was conducted to see whether men or women

superintendents make more money. This is a

a. quasi, between subject design

b. quasi, within subject design

c. true, between subject design

d. true, within subject design

A survey was conducted to see whether men or women

superintendents make more money. This is a

a. one-tailed test

b. two-tailed test

c. three-tailed test

d. not enough information

A survey was conducted to see whether men or women

superintendents make more money. The null hypothesis is

a. men make more money

b. women make more money

c. no difference between amount of money made

d. there is a difference between the amount of money made

- A survey was conducted to see whether men or women
- superintendents make more money. If the null hypothesis was rejected we will conclude that
- a. men make more money
- b. women make more money
- no difference between amount of money made
- d. there is a difference between the amount of money made

- A survey was conducted to see whether men or women
- superintendents make more money. A Type I error would be
- a. claiming men make more money, when they don’t
- b. claiming women make more money, when they don’t
- claiming no difference between amount of money made, when there is a difference
- d. claiming there is a difference between the amount of money made, when there is no difference

- A survey was conducted to see whether men or women
- superintendents make more money. A Type II error would be
- a. claiming men make more money, when they don’t
- b. claiming women make more money, when they don’t
- claiming no difference between amount of money made, when there is a difference
- d. claiming there is a difference between the amount of money made, when there is no difference

An t-test was conducted, there were ___ men in the study

and ___ women.

a. 18; 21

b. 21; 18

c. 19; 19

d. 38; 38

Let’s try one

A t-test was conducted, which of the following best describes

the results:

a. t(21) = 2.02; p < 0.05

b. t(21) = 2.02; n.s.

c. t(37) = 5.0; p < 0.05

d. t(37) = 5.0; n.s

Let’s try one

A t-test was conducted, with a two tail test was there a significant difference?

a. No, because 5.0 is not bigger than 6.89

b. Yes, because 5.0 is bigger than 1.68.

c. Yes, because 5.0 is bigger than 1.37

d. Yes, because 5.0 is bigger than 2.02

Let’s try one

Which is true

a. p < 0.05

b. p < 0.01

c. p < 0.001

d. All of the above

Let’s try one

A survey was conducted to see whether women

superintendents make more money than men. This is a

a. one-tailed test

b. two-tailed test

c. three-tailed test

d. not enough information

Note the change in the problem

A survey was conducted to see whether women superintendents make more money than men. A t-test was conducted, which of the following best describes the results:Note the results were in the unpredicted direction

a. reject the null

b. do not reject the null

c. not enough information

Let’s try one

A survey was conducted to see whether women superintendents make more money than men. A t-test was conducted, which of the following best describes the results: Note the results were in the unpredicted direction

a. t(21) = 2.02; p < 0.05

b. t(21) = 2.02; n.s.

c. t(37) = 5.0; p < 0.05

d. t(37) = 5.0; n.s

Let’s try one

Study Type 1: Confidence Intervals

Comparing Two Means?

Use a t-test

Study Type 2: t-test

We are looking to compare two means

http://www.youtube.com/watch?v=n4WQhJHGQB4

We are looking to compare two means

Study Type 2: t-test

Study Type 3: One-way Analysis of Variance (ANOVA)

Comparing more than two means

Study Type 3: One-way ANOVA

Single Independent Variable comparing more than twogroups

Single Dependent Variable (numerical/continuous)

Used to test the effect of the IV on the DV

Ian was interested in the effect of incentives for girl scouts on the number

of cookies sold. He randomly assigned girl scouts into one of three groups.

The three groups were given one of three incentives and looked to see who

sold more cookies. The 3 incentives were 1) Trip to Hawaii, 2) New Bike

or 3) Nothing. This is an example of a true experiment

How could we

make this a

quasi-experiment?

Independent Variable: Type of incentive

Levels of Independent Variable: None, Bike, Trip to Hawaii

Dependent Variable: Number of cookies sold

Levels of Dependent Variable: 1, 2, 3 up to max sold

Between participant design

Causal relationship: Incentive had an effect – it increased sales

Study Type 3: One-way ANOVA

Single Independent Variable comparing more than two groups

Single Dependent Variable (numerical/continuous)

Used to test the effect of the IV on the DV

Ian was interested in the effect of incentives for girl scouts on the number

of cookies sold. He randomly assigned girl scouts into one of three groups.

The three groups were given one of three incentives and looked to see who

sold more cookies. The 3 incentives were 1) Trip to Hawaii, 2) New Bike

or 3) Nothing. This is an example of a true experiment

Dependent

variable is always quantitative

Sales per

Girl scout

Sales per

Girl scout

New

Bike

None

Trip

Hawaii

New

Bike

None

Trip

Hawaii

In an ANOVA, independent variable is qualitative

(& more than two groups)

One-way ANOVA versus Chi Square

Be careful you are not designing a Chi Square

If this is just frequency you may have a problem

This is an

Chi Square

Total Number

of Boxes Sold

Sales per

Girl scout

This is an ANOVA

New

Bike

None

Trip

Hawaii

New

Bike

None

Trip

Hawaii

These are just frequencies

These are just frequencies

These are just frequencies

These are means

These are means

These are means

Writing Assignment

- To prepare for our ANOVA Project - Due April 11th
- There are five parts
- 1. A one page report of your design (includes all of the information from the writing assignment)
- Describe your experiment: what is your question / what is your prediction?
- State your Independent Variable (IV), how many levels there are, and the operational definition
- State your Dependent Variable (DV), and operational definition
- How many participants did you measure, and how did you recruit (sample) them
- Was this a between or within participant design (why?)

- 2. Gather the data
- Try to get at least 10 people (or data points) per level
- If you are working with other students in the class you should have 10 data points per level for each member of your group

- 3. Input data into Excel (hand in data)
- 4. Complete ANOVA analysis hand in ANOVA table
- 5. Statement of results and include a graph of your means

Thank you!

See you next time!!